Install And Load The Kernsmooth R Package. What Does The Copyright Message Say?

If you install and load the R package – KernSmooth you will get the following copyright message : Copyright M. P. Wand 1997-2009. More information can be found out here : https://launchpad.net/ubuntu/precise/+source/kernsmooth/+copyright

What is base R focus?

This package contains the basic functions which let R function as a language: arithmetic, input/output, basic programming support, etc. Its contents are available through inheritance from any environment. For a complete list of functions, use library(help = ‘base’).

Which is a characteristic of a good question when posting to message boards?

A good question is framed in a clear, easily understandable language, without any vagueness. Students should understand what is wanted from the question even when they don’t know the answer to it.

Can I use R for commercial purposes?

It is the opinion of the R Core Team that one can use R for commercial purposes (e.g., in business or in consulting). The GPL, like all Open Source licenses, permits all and any use of the package. It only restricts distribution of R or of other programs containing code from R.

What license should I use for my R package?

To avoid this problem it’s generally recommended to license your package as GPL >=2 or GPL >= 3 so that future versions of the GPL license also apply to your code.

Which of the following are included in R packages?

R is distributed with fourteen ‘base packages’: base, compiler, datasets, grDevices, graphics, grid, methods, parallel, splines, stats, stats4, tcltk, tools, and utils.

What does RStudio do?

RStudio is an integrated development environment (IDE) for R. It includes a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging and workspace management.

Is tidyverse better than base R?

Tidyverse is easy to use if you are familiar with it, the codes might be more readable than those written in base R, but you will surely encounter problems that you have to solve in base R. Or that are easier to solve, or that are easier to find solution for.

What makes a question a good question?

Good questions are often open-ended, meaning they defy yes-or-no responses and encourage long, free-form answers. Open-ended questions usually result in expansive discussions that address not only the topic but also tangential issues.

What are characteristics of a good questionnaire?

Qualities of a Good Questionnaire

  • The length of questionnaire should be proper one.
  • The language used should be easy and simple.
  • The term used are explained properly.
  • The questions should be arranged in a proper way.
  • The questions should be in logical manner.
  • The questions should be in analytical form.
  • Which of the following is a characteristic of a researchable question?

    The characteristics of a good research question, assessed in the context of the intended study design, are that it be feasible, interesting, novel, ethical, and relevant (which form the mnemonic FINER; Table 2.1).

    What is the correct way to install the packages in R?

    Alternatively, you can install R packages from the menu.

    1. In RStudio go to Tools → Install Packages and in the Install from option select Repository (CRAN) and then specify the packages you want.
    2. In classic R IDE go to Packages → Install package(s), select a mirror and install the package.

    How do you cite R packages?

    citation() To cite R in publications use: R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.

    Is RStudio free for commercial use?

    The RStudio IDE open-source product is free under the Affero General Public License (AGPL) v3. The RStudio IDE is also available with a commercial license and priority email support from RStudio, Inc.

    Are there any examples of copyrighted R packages?

    The exceptions are rare and the companies behind any copyrighted packages with any special usage restrictions will probably make it quite clear. I actually tried to find an example of a truly copyrighted R package. I was going to use one by Revolution Analytics, but from what I can see even theirs is just under Apache 2.0 license.

    KernSmooth: Functions for Kernel Smoothing Supporting Wand & Jones (1995)

    Reverse depends: BGmix, bioDist, BwQuant, CHsharp, DeMAND, DeMixT, FCGR, gammSlice, gb, GenKern, GoFKernel, ICE, IsoCI, KCsmart, LiebermanAidenHiC2009, mpm, mpmi, MPV, PlotContour, qualV, sharpData, sharpPen, TPmsm Reverse imports: adegraphics, ATACseqQC, BayLum, BiplotGUI, CAGEr, CausalGPS, cholera, classInt, CoMiRe, compcodeR, condSURV, curvHDR, dearseq, diveMove, DRDRtest, DSWE, DVHmetrics, earlywarnings, FactoClass, FiSh, flowStats, flowViz, ftsa, ggalt, gplots, hdrcde, heatmaps, HiCcompare, hoardeR, HRW, IFC, IndexConstruction, INSPEcT, IWTomics, jocre, kamila, kdevine, kernplus, ks, localIV, lumi, Mercator, methylKit, mgcViz, miRcomp, mxnorm, NCA, Patterns, philentropy, prodlim, promotionImpact, quantCurves, quantdr, r2d2, ramwas, rassta, rcrimeanalysis, rddtools, reconsi, refreg, robsurvey, Rtrack, SCBmeanfd, SensoMineR, seqPattern, sesame, Seurat, shazam, shotGroups, singleCellTK, siqr, spd, stpp, survcomp, survidm, synRNASeqNet, tofsims, TSclust, TSdist, viper Reverse suggests: AER, amt, aroma.core, bayestestR, colorspace, copula, cutpointr, DHARMa, EDASeq, gamclass, HistData, HSAUR, HSAUR2, HSAUR3, integIRTy, kernelboot, LaplacesDemon, latentnet, lattice, lava, lessR, mapsFinland, MRS, MSG, MVA, NCmisc, openair, pagoda2, PBSmodelling, prettydoc, RGraphics, rstan, scCB2, siggenes, simsem, sparrow, stm, survey, tidybulk, tidyvpc, trio, vcd

    The Data Scientist’s Toolbox Quiz 2 (Week 2) John Hopkins Data Science Specialization Coursera for the github repo

    The following is the GitHub repository for the remainder of the specialization: Data Science Coursera is a free online course that teaches data science concepts.

    Question 1

    • A directory named data in your current working directory will be created if you run one of the following commands. create the directory /Users/data
    • create the directory data
    • pwd data
    • create the directory data

    Answer: mkdir data

    Question 2

    • Which of the following will begin the process of creating a git repository on your computer? the commands git merge, git pull, git init, and git push

    Answer: git init

    Question 3

    • Consider the following scenario: you have cloned a repository named datascientist on Github, however the repository is not yet available on your local machine. Which of the following commands will copy the contents of the directory to your computer’s hard drive? The next question will be answered assuming that your user name is username. git pull datascientist master
    • git clone datascientist master
    • git init
    • git pull datascientist master
      Answer:

    • git clone

    Question 4

    A markdown document with a secondary heading that reads ″Data Science Specialization″ and an unordered list with the following bullet points will be created using which of the following?R is used, and there are nine classes.It takes raw data and turns it into data products.Option 1: *h2 Data Science Specialization * Makes use of R * Comprises nine courses * Covers the entire data lifecycle from raw data to data product Option 2: Data Science Specialization * Makes use of the R programming language * Comprises nine courses * Covers everything from raw data to data products Specialization in Data Science is the third option.R is used in nine courses, and the course progresses from raw data through data products.

    1. Data Science Specialization (R) (nine courses) * Covers the whole data lifecycle from raw data through data products Option 4: Option 5: Data Science Specialization * Makes use of the R programming language * Comprises nine courses * Covers everything from raw data to data products A data science specialty that uses R and includes nine courses that takes you from raw data to data products is your best bet.

    Question 5

    • Install and load the KernSmooth R package from the command line. Is there anything in the copyright message? COPYRIGHT KernSmooth 1997-2009
    • COPYRIGHT M. P. Wand 1997-2013
    • COPYRIGHT M. P. Wand 1997-2009
    • COPYRIGHT Coursera 2009-2013
    • COPYRIGHT Coursera 2009-2013
    • COPYRIGHT Coursera 2009-2013

    The Install.packages(″KernSmooth″) library is used to install packages (″KernSmooth″) M. P. Wand was the author of the answer from 1997 to 2009.

    Question 1

    • A directory named data in your current working directory will be created if you run one of the following commands. create the directory /Users/data
    • create the directory data
    • pwd data
    • create the directory data

    Answer: mkdir data

    Question 2

    • Which of the following will begin the process of creating a git repository on your computer? the commands git merge, git pull, git init, and git push

    Answer: git init

    Question 3

    • Consider the following scenario: you have cloned a repository named datascientist on Github, however the repository is not yet available on your local machine. Which of the following commands will copy the contents of the directory to your computer’s hard drive? The next question will be answered assuming that your user name is username. git pull datascientist master
    • git clone datascientist master
    • git init
    • git pull datascientist master
      Answer:

    • git clone

    Question 4

    A markdown document with a secondary heading that reads ″Data Science Specialization″ and an unordered list with the following bullet points will be created using which of the following?R is used, and there are nine classes.It takes raw data and turns it into data products.Option 1: *h2 Data Science Specialization * Makes use of R * Comprises nine courses * Covers the entire data lifecycle from raw data to data product Option 2: Data Science Specialization * Makes use of the R programming language * Comprises nine courses * Covers everything from raw data to data products Specialization in Data Science is the third option.R is used in nine courses, and the course progresses from raw data through data products.

    1. Data Science Specialization (R) (nine courses) * Covers the whole data lifecycle from raw data through data products Option 4: Option 5: Data Science Specialization * Makes use of the R programming language * Comprises nine courses * Covers everything from raw data to data products A data science specialty that uses R and includes nine courses that takes you from raw data to data products is your best bet.

    Question 5

    • Install and load the KernSmooth R package from the command line. Is there anything in the copyright message? COPYRIGHT KernSmooth 1997-2009
    • COPYRIGHT M. P. Wand 1997-2013
    • COPYRIGHT M. P. Wand 1997-2009
    • COPYRIGHT Coursera 2009-2013
    • COPYRIGHT Coursera 2009-2013
    • COPYRIGHT Coursera 2009-2013

    The Install.packages(″KernSmooth″) library is used to install packages (″KernSmooth″) M. P. Wand was the author of the answer from 1997 to 2009.

    DSS/DSS1Week1Quiz.tex at master · DragonflyStats/DSS

    \documentclass
    \usepackage
    \usepackage
    \begin
    \section*
    % Data Scientist Toolkit Week 1
    %-%
    \newpage
    \subsection*
    Which of the following are courses in the Data Science Specialization? \\ Select all that apply.
    \begin
    \item R programming
    \item The Elements of Statistical Learning
    \item Statistical Inference
    \item Data Science 101
    \end
    \bigskip
    \begin
    \end
    %-%
    \newpage
    \subsection*
    \begin
    \centering
    \includegraphics
    \end
    \newpage
    \begin
    \centering
    \includegraphics
    \end
    %-%
    \newpage
    \subsection*
    Why are we using \texttt for the course track? Select all that apply.
    \begin
    \item \texttt is free.
    \item \texttt has a nice IDE, Rstudio.
    \item \texttt allows object oriented programming.
    \item \texttt is the best cloud computing language.
    \end
    Remark: the fact that R can do something, doesnt mean R was chosen for this course for that reason.
    \begin
    R Language Definition
    \end
    %-%
    \newpage
    \subsection*
    % Using Help Files
    % Intro to R
    % Stack Overflow
    % Rseek
    %Rstats
    % DataCamp
    % SWIRL
    \begin
    \itemUsing Help Files (for example \texttt)
    \item Introduction to R (command \texttt – top left of page)
    \item Stack Overflow (stackoverflow.com)
    \item Rseek.org
    \item Using Twitter: \texttt
    \item www.datacamp.com
    \item SWIRL
    \end
    %-%
    \newpage
    %Learning Resources
    \subsection*
    What are good ways to find answers to questions in this course track? Select all that apply.
    \begin
    \item Searching Google.
    \itemPosting homework assignments to mailing lists
    \item Looking through \texttt help files.
    \item Expecting every answer to be in a lecture slide
    \end
    %-%
    \newpage
    % Question 4
    % Appropriate Behaviour
    \subsection*
    What are characteristics of good questions on the message boards? \\ Select all that apply.
    \begin
    \item Is polite and courteous.
    \item Provides no details.
    \item Explicitly lists versions of software being used.
    \item is insulting or rude.
    \end
    %-%
    \newpage
    \subsection*
    \begin
    \item The Comprehensive R Archive Network
    \item
    \end
    \subsection*
    %-%
    \newpage
    \subsection*
    Which of the following packages provides Machine Learning Functionality
    \begin
    \item \textbf}
    \item \textbf}
    \item \textbf}
    \item \textbf}
    \end
    %-%
    \newpage
    \subsection*
    Following from Question 5, search the CRAN package repository to find a package
    related to each of the following topics.
    \begin
    \item Graphics
    \item Biology
    \item Archeology
    \item Marine or Maritime Sciences
    \item Medical Imaging
    \item Missing Data
    \item Quality Control
    \item Social Sciences
    \item Text Analytics
    \end
    %-%
    \newpage
    \subsection*
    \textbf
    \end
    \subsection*
    %-%
    Question 5
    Which of the following packages provides machine learning functionality? Select all that apply
    \item knitr
    \item filehash
    \item gbm
    \item kernlab
    %-%
    \subsection*
    We take a random sample of individuals in a population and identify whether they smoke and if they have cancer. We observe that there is a strong relationship between whether a person in the sample smoked or not and whether they have lung cancer.
    \\
    \\
    We claim that the smoking is related to lung cancer in the larger population. We explain we think that the reason for this relationship is because cigarette smoke contains known carcinogens such as arsenic and benzene, which make cells in the lungs become cancerous.
    \begin
    \item This is an example of a causal data analysis.
    \item This is an example of a predictive data analysis.
    \item This is an example of an inferential data analysis.
    \item This is an example of a mechanistic data analysis.
    \end
    \newpage
    %-%
    \subsection*
    What is the most important thing in Data Science?
    \begin
    \item Statistical inference.
    \item Machine learning and prediction.
    \item The data.
    \item The question you are trying to answer.
    \end
    \newpage
    \section*
    %-%
    \subsection*
    Which of the following commands will create a directory called data in your current working directory?
    \begin
    \item mkdir data
    \item pwd./data
    \item pwd data
    \item mkdir /Users/data
    \end
    %-%
    \subsection*
    Which of the following will initiate a git repository locally?
    \begin
    \itemgit merge origin master
    \itemgit push
    \itemgit boom
    \itemgit init
    \end
    %-%
    \subsection*
    Suppose you have forked a repository called datascientist on Github but it isn’t on your local computer yet. Which of the following is the command to bring the directory to your local computer? (For this question assume that your user name is username)
    \begin
    \item git pull datascientist master
    \item git init
    \item git push origin master
    \item git clone
    \end
    %-%
    \subsection*
    Which of the following will create a markdown document with a secondary heading saying ″Data Science Specialization″ and an unordered list with the following for bullet points: Uses R, Nine courses, Goes from raw data to data products
    \begin
    \begin
    *h2 Data Science Specialization
    * Uses R
    * Nine courses
    * Goes from raw data to data products
    \end
    \end
    \begin
    \begin
    Data Science Specialization
    li Uses R
    li Nine courses
    li Goes from raw data to data products
    \end
    \end
    \begin
    \begin
    *** Data Science Specialization
    * Uses R
    * Nine courses
    * Goes from raw data to data products
    \end
    \end
    \begin
    \begin
    Data Science Specialization
    * Uses R
    * Nine courses
    * Goes from raw data to data products
    \end
    \end
    \begin
    \begin
    Data Science Specialization
    * Uses R
    * Nine courses
    * Goes from raw data to data products
    \end
    \end
    %-%
    \subsection
    Install and load the KernSmooth R package. What does the copyright message say?
    \begin
    \item Copyright M. P. Wand 1990-2009
    \item Copyright M. P. Wand 1997-2009
    \item Copyright Coursera 2009-2013
    \item Copyright Matthew Wand 1997-2009
    \end
    \newpage
    \end

    CourseraCourses/Quiz2.Rmd at master · dreamcrash/CourseraCourses

    Permalink Cannot retrieve contributors at this time This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode characters Show hidden characters
    Course 1 – The Data Scientist’s Toolbox:
    Quiz 2:
    Question 1
    Which of the following commands will create a directory called data in your current working directory?
    **Answer:**
    – mkdir data;
    Question 2
    Which of the following will initiate a git repository locally?
    **Answer:**
    – git init;
    Question 3
    Suppose you have forked a repository called datascientist on Github but it isn’t on your local computer yet. Which of the following is the command to bring the directory to your local computer? (For this question assume that your user name is username)
    **Answer:**
    – git clone
    Question 4
    Which of the following will create a markdown document with a secondary heading saying ″Data Science Specialization″ and an unordered list with the following for bullet points: Uses R, Nine courses, Goes from raw data to data products
    **Answer:**
    – \ \Data Science Specialization
    \* Uses R
    \* Nine courses
    \* Goes from raw data to data products
    Question 5
    Install and load the KernSmooth R package. What does the copyright message say?
    **Answer:**
    – Copyright M. P. Wand 1997-2009;

    7 Characteristics Of A Good Question

    Briefly stated, educators and eLearning experts are regularly assigned the responsibility of question production as part of the preparation of quizzes for their programs. This article discusses some of the most important aspects of effective questions, as well as several tools that may be used to assist in the design of questions and quizzes.

    What The Characteristics Of A Good Question Are

    ″If I had an hour to solve a problem and my life relied on the solution, I would spend the first 55 minutes figuring out what the right question to ask would be…″ For as long as I understand the question, I should be able to do the task in less than five minutes.″ Albert Einstein was a scientist and inventor.That demonstrates how important a well-phrased query is.It is a potent weapon that may have the intended effect and elicit the appropriate feelings and ideas in the user.A well-crafted question may inspire kids’ imaginations while simultaneously educating them.While writing effective questions may appear to be a fairly basic undertaking, it is not as straightforward as it appears at first glance.

    1. A good question must have its own distinct personality, which is comprised of a number of distinct traits.
    2. In order to construct an effective inquiry, consider the following characteristics.

    1. Relevant

    A pertinent query is one that is well-phrased.It focuses on recalling only the information provided in your session and is well-aligned with the learning objectives for the whole course.If you pose a question like ‘What are the branches of soil science?’ during an introductory lecture that just focuses on the kind of soil, you aren’t actually asking a topic that is relevant to the course.When asked this question, it would be preferable and more relevant to respond with the following: ‘What are the features of each type of soil?’

    2. Clear

    A good question is one that is phrased in a straightforward, easily comprehensible manner, with no ambiguity.Even if they don’t know the solution to the question, students should be able to deduce what the question is asking them to accomplish.Because it does not specify whose rights are sought, the inquiry ‘What are your rights?’ may be interpreted as confusing and imprecise by some.On the other hand, if you pose the question ‘What fundamental rights are protected by the Universal Declaration of Human Rights?’, the same question becomes crystal plain and precise.

    3. Concise

    A excellent inquiry is generally succinct and to the point.It does not provide any extraneous material that would have pupils spending more time trying to comprehend it correctly.The goal is not to deceive students, but rather to test their knowledge.Consider the following question: ‘Because canine distemper affects numerous body systems, including the gastrointestinal tract, the respiratory tract, the spinal cord, and the brain, how should canines be treated for it?’ This question provides a great deal more information than is actually necessary.It might easily be rephrased as ‘How should distemper be treated in canines?’ or something similar.

    4. Purposeful

    A query that does not have a clear objective is of no use.The purpose assists in evaluating the query in relation to certain predetermined standards.A well-crafted inquiry can elicit both inherent and specific information from the respondent.A question such as ‘What is the capital of France?’ necessitates the student just using their memory in order to provide an answer.And, if that is the goal you have set for yourself, the query is completely legitimate in its tone.

    1. However, if the goal is to assess and improve the student’s capacity to reason, the same question could need to be phrased as ‘How is Paris ideally situated to serve as the capital of France?’

    5. Guiding But Not Leading

    A well-crafted inquiry directs the learners toward a better grasp of the subject shown in the picture.However, at the same time, it does not lead them to precise conclusions.For example, the question ‘Since infant formula is a safe alternative for breastfeeding, should its usage be normalized?’ is a strongly biased question since it presupposes that formula is safe for newborns and drives users to respond affirmatively.As an alternative, this question might simply be rephrased as ‘Do you believe that the usage of infant formula should be made more widespread?’

    6. Stimulates Thinking

    A excellent question asks students to think about and recollect the topics that have been presented to them.It does not treat them as though they are beneath them by asking the obvious.’Can you exist without water?’ is an excellent example of a question that does not provoke any thought since it challenges a commonly acknowledged truth.’How long does it take for someone to die from a lack of water?’ could be a more appropriate question in this situation.

    7. Single-Dimensional

    Keep in mind that a query is just a question.As a result, a good inquiry is one that focuses on only one dimension at a time.Whenever there are several concepts to examine, it is preferable to break them down into separate questions.″When did World War II begin and why was it fought?″ is a multi-dimensional question that needs students to consider two different aspects of the same topic at the same time.It is advised that it be divided into two separate questions to make it simpler to remember and to make it easier to understand: ‘When did World War II begin?’ and ‘Why was World War II fought?’ Excellent questions, their potency, and the qualities of good questions are all important considerations.

    1. Interestingly, if you are regularly assigned with the formulation of questions, you no longer have to rely only on manual efforts.
    2. You might make advantage of automatic question creation solutions that are driven by artificial intelligence.
    3. And, once you’ve compiled a large number of high-quality questions for your question collection, have a look at this informative article on creating great learning quizzes.
    4. I hope you found this post to be interesting.
    1. I’d be interested in hearing your comments.

    Chapter 9 Licensing

    • The purpose of this chapter is to provide you with the fundamental tools for managing the licensing of your R package. Software licensing is a huge and sophisticated area that is made even more convoluted by the fact that it is located at the confluence of programming and legal principles. For the most part, you don’t need to be an expert to do the right thing: respecting how an author wishes their code to be used, as indicated by the license they choose to distribute it under. It is necessary to understand the two primary camps of open source licenses in order to comprehend the author’s intentions: Permissive licenses are quite simple to obtain. Code distributed under a permissive license can be freely copied, updated, and published, with the sole constraint being that the license must be kept at all times. The MIT and Apache licenses are the most widely used permissive licenses today
    • earlier permissive licenses, such as the different variations of the BSD license, are also widely used
    • copyleft licenses are more stringent than permissive licenses. The GPL is the most widely used copyleft license, and it enables you to freely copy and alter the code for personal use. However, if you publish updated versions or bundle with other work, the modified version or entire bundle must also be licensed under the GPL.

    In order to acquire a high-level overview of the open source licensing landscape, along with the specifics of various licenses, I highly recommend checking out the resources I’ve included in the links above.Across all programming languages, permissive licenses are the most prevalent type of license to come across.For example, according to a 2015 study of GitHub repositories, 55 percent of them were licensed under a permissive license, whereas just 20 percent were licensed under a copyleft license.However, the R community differs in that, as of 2020, my investigation (in response to Sean Kross’s blog article) discovered that over 70% of CRAN packages use a copyleft license and approximately 15% use a permissive license (see Figure 1).In this chapter, you will learn how to license your own code, and then you will learn the most crucial information about accepting code from other people (for example, through a pull request) and bundling other people’s work into your package.

    1. It’s important to note that merely using a package or R itself does not obligate you to comply with the licensing terms; this is why you may create private R code and why R packages can be distributed under whatever license you want.
    2. It is recommended that you read Colin Fay’s book Licensing R for further information on how to license R packages.
    3. Please ensure that you are using usethis 2.0.0 or greater when running the code in this chapter; creating this chapter generated a number of changes in the usethis package.)

    Code you write

    • As a starting point, we’ll discuss code that you’ve written and how to license it so that it’s obvious how you want other people to treat it. In a nutshell: If you want a permissive license that allows anyone to use your work with the very minimum of limitations, use the use mit license() function to select the MIT license.
    • Using use gpl license(), you may pick the GPLv3 license, which ensures that any derivatives and bundles of your code are likewise open source
    • alternatively, you can use the BSD license.
    • CC0 license with use cc0 license is a good choice if your package comprises mostly data rather than code and you want to place the fewest limitations on it (). Alternatively, if you wish to demand credit when your data is utilized, you may use the CC BY license by executing the use ccby license() function.
    • If you do not want your code to be released under an open source license, use the use proprietary license function (). CRAN does not allow the distribution of such packages.

    In Section 9.2.2, we’ll go through some of the specifics and introduce a few of other licenses.It is critical to utilize a license because if you do not, the default copyright rules will apply, which means that no one will be able to produce a duplicate of your code without your express consent.Although it is possible to license a CRAN package under a non-open source license such as the ACM license, we do not suggest doing so.

    Copyright holder

    • The phrase ″copyright holder″ must be defined first before we proceed any further since it is extremely significant. The copyright holder (or holders) are the individuals who own the underlying copyright to the code and, as a result, are the only individuals who have the authority to pick (and subsequently alter) the license for the code. There are three major scenarios. You’ll need to double-check your local regulations, at the very least in the United States. ″>21: If you created the code on your own time, you are the owner of the intellectual property rights.
    • If you developed the code for your employer, your employer is the owner of the intellectual property rights.
    • When writing code for contract work, unless the contract clearly states otherwise, you are considered the copyright holder.

    Therefore, if you’re building software for your employer, you’ll need to gain their approval on the open source license you choose to utilize.Some organizations (especially colleges) have standard procedures, which means you won’t have to get permission every time you want to use a computer; nevertheless, you’ll need to find out what your company’s policy is beforehand.It is important to note that if numerous persons or companies have contributed to the package, there will be multiple copyright holders: each individual or corporation will have the copyright for the specific contribution to which they have made.We’ll come back to this subject in Section 9.4 of the book.

    Key files

    • There are three main files that are used to keep track of your license decisions: The License field in the DESCRIPTION is specified by each individual license. This file includes the name of the license in a standard format, which allows R CMD check and CRAN to automatically validate the validity of the license. It is available in four different configurations: An identifier and version definition, for example, GPL (>= 2) or Apache License (= 2.0)
    • The use of a standard abbreviation such as GPL-2, LGPL-2.1, or Artistic-2.0.
    • A name for a license ″template,″ as well as the name of a file that contains special variables. The most typical situation is MIT + file LICENSE, in which case the LICENSE file must have two fields: the year and the copyright holder
    • Pointer to the full text of a non-standard license, file LICENSE
    • Pointer to the full text of a standard license, file LICENSE
    • There are more complex license arrangements that can be used, but they are outside the scope of this work. For further information, see the Licensing section of R-exts.
    • As previously stated, the LICENSE file can be utilized in one of two ways. Some licenses are templates that require extra information to be included into the LICENSE file in order to be valid. It is also possible for the LICENSE file to contain the complete text of licenses that are not standard or open source. You are not authorized to include the complete text of standard licenses
    • nevertheless, LICENSE.md contains a copy of the license’s full text. The inclusion of a copy of the license is required under all open source licenses
    • however, because CRAN does not allow you to include a copy of standard licenses in your package, we utilize them as well. Rbuildignore is used to ensure that this file is not sent to CRAN.

    A single additional file will be discussed further in Section 9.4.2, and that is LICENSE. note. Using this technique is useful when you have bundled code authored by others, but only some components of your package are licensed more permissively than the entire package.

    More licenses

    • Above, I provided you with the exact bare minimal information you require. However, it is worthwhile to highlight a few other key licenses, which are listed below in descending order from most liberal to least permissive: use mit license(): With the MIT license, you are just required to preserve the copyright and license notice
    • this is by far the most lenient license.
    • Use apache license(): The Apache License is comparable to the MIT license in that it contains an explicit patent grant. However, the Apache License is more restrictive. The patent system is a separate component of intellectual property from copyrights, and some organizations are concerned about protecting their intellectual property against patent claims.
    • The LGPL is a little more permissive than the GPL, enabling you to bundle LPGL code and use any license you want for the bigger work
    • use lgpl license() allows you to bundle LPGL code and use any license you want for the larger work
    • Use gpl license(): We’ve already spoken about the GPL, but there’s one essential detail to keep in mind: there are two primary versions of the GPL, GPLv2 and GPLv3, and they are not compatible with one another (i.e., you can’t combine GPLv2 and GPLv3 code in the same project). The GPL version 2 or 3 is typically suggested for avoiding this problem since it ensures that your code will be compatible with future versions of the GPL license. To prevent this difficulty, it’s generally advised to license your package as GPL version 2 or 3. This is exactly what the use gpl license() function does by default
    • When distributing software over a network, the AGPL defines distribution as delivering a service over the network. This means that if you utilize AGPL code to create a web service, all of the packaged code must likewise be open-sourced. In light of the fact that this is a somewhat larger claim than the GPL, many businesses specifically prohibit the usage of AGPL software.

    There are a plethora of additional licensing options available. The most popular ones can be found at and the whole list can be found at. The biggest disadvantage of selecting a license that is not included in the bullet list above is that fewer R users will be familiar with what it entails.

    Relicensing

    When choosing your initial license, it’s crucial to think carefully about your options because changing your license later on may be difficult because it necessitates the consent of all copyright holders.The copyright holders include everyone who has contributed a significant amount of code, unless you’ve done something unusual (which we’ll explore in Section 9.3), in which case the copyright holders include you.If you find yourself in the position of having to re-license a product, we propose the following procedure:

    1. To make sure that the package does not contain bundled code (which we’ll discuss in Section 4.3), look at the [email protected] field in the DESCRIPTION.
    2. Look through the Git history or the contributors display on GitHub to find all of the contributors.
    3. It is also possible to examine the particular contributions and exclude persons who simply supplied typo repairs and other similar efforts.
    4. Simple contributions, such as typo repairs, are often not protected by copyright since they are not considered to be original works of art. However, even a single line can be deemed a piece of original work, therefore err on the side of caution and, if in doubt, leave the contributor in.″>22
    5. Inquire with each contributor to see whether they are in favor of altering the license. If every contributor is on GitHub, the quickest and most straightforward approach to accomplish this is to open an issue in which you name all contributors and ask them to affirm that they are in agreement with the modification before moving further. Generics and covr are two instances of code that has been relicensed by the tidyverse team.
    6. Once all of the copyright holders have given their approval, make the adjustment by invoking the relevant licensing function on your computer.

    Data

    • Due to the fact that open source licenses are intended to apply only to source code, you should choose another type of license for your package if it is largely comprised of data. One of two Creative Commons licenses comes highly recommended: In order to make the data as widely available as possible, you should utilize the CC0 license, which may be accomplished with the help of the use cc0 license function (). Essentially, this is a liberal license that is identical to the MIT license (with the exception that it pertains to data rather than code)
    • Using the CC-BY license in conjunction with use ccby license(), you may ensure that your data is properly attributed when it is used by others.

    Code given to you

    • Many packages contain code that was not authored by the package’s creator.
    • Other individuals may choose to contribute to your package through the use of a pull request or similar mechanism, or you may discover some code and decide to bundle it with your package.
    • This part will cover code that has been provided to you by others, while the following section will cover code that you have bundled.
    • When someone contributes code to your package through a pull request or other similar mechanism, you can presume that the author is agreeable for their work to be distributed under the terms of your license.

    These terms of service are explicitly stated in the GitHub terms of service, however they are commonly accepted to be accurate regardless of how code is submitted.Providing a developer certificate of origin is required by certain organizations who are extremely risk conservative, but this is uncommon in general, and I haven’t seen it requested in the R community.″>23.

    1. It should be noted, however, that unless you employ a ″contributor licensing agreement,″ or CLA, the author maintains ownership of their code.
    2. Using a CLA has several advantages, the most important of which is that it simplifies the copyright of the code and so makes it simple to relicense code when necessary.
    3. This is particularly important for projects that are both open-source and commercial in nature because it makes it simple to implement dual licensing, in which the code is made available to the public under a copyleft license and made available to paying customers under a different, more permissive license.

    The importance of acknowledging the contribution cannot be overstated, and it is considered good practice to be liberal with gratitude and citation.As part of the tidyverse, we require that all code contributors add their GitHub username as part of the NEWS.md file, and we acknowledge all contributors in the releases that they contribute to.We exclusively hire core developers, which are those who are responsible for ongoing development.

    This is best expressed in the GOVERNANCE.md file, which contains the ggplot2 governance documentation ″>24 to the DESCRIPTION file; but, some projects want to include all contributors, no matter how little their contributions are.

    Code you bundle

    • There are three typical reasons why you would decide to bundle code authored by someone else: convenience, security, and performance. If you’re creating a nice and functional web page or HTML widgets, you’re including someone else’s CSS or JS library.
    • Essentially, you’re creating a R wrapper around a basic C or C++ library. (When dealing with complicated C/C++ libraries, it is common practice not to include the code in your package but rather to link to a copy that is already installed on the system.)
    • You’ve copied a little piece of R code from another package in order to prevent becoming reliant on that package. Most of the time, relying on another package is the best course of action since it eliminates the need to worry about licensing and ensures that you will immediately get bug patches. However, there are situations when you just want a tiny piece of code from a large package, and copying and pasting it into your package is the best course of action
    • for example,

    It should be noted that R differs from other programming languages such as C, in which the most frequent method of bundling code is by compilation into a single executable.

    License compatibility

    • Before you include someone else’s code in your package, you must first ensure that the code’s licensing is compliant with your own license terms and conditions. When sharing code, you have the option of adding extra constraints, but you cannot remove restrictions, which implies that licensing compatibility is not symmetric in this situation. It is possible to bundle MIT licensed code in a GPL licensed package, but it is not possible to bundle GPL licensed code in an MIT licensed package, and vice versa. It is necessary to evaluate the following five scenarios: If your license and their license are the same, it is permissible to combine the two products.
    • If they have an MIT or BSD license, it is permissible to package them.
    • If their code is licensed under a copyleft license and your code is licensed under a permissive license, you are not permitted to bundle their code. Alternative approaches, such as hunting for code with a more liberal license or packaging the external code in a separate package, will need to be considered.
    • Because Stack Overflow licenses its code under the Creative Commons CC BY-SA license, it is only compliant with GPLv3 if the source comes from Stack Overflow. As a result, you should exercise caution when incorporating Stack Overflow code into open source products. More information may be found at Otherwise, you’ll have to perform some preliminary study. The graphic on Wikipedia is useful, and Google is your best buddy. It’s crucial to remember that multiple versions of the same license are not always compatible with one another, for example, GPLv2 and GPLv3 are not compatible.

    If your product is not open source, the situation becomes more hard to deal with. It is still simple to get permissive licenses, and copyleft licenses are often unrestrictive in their application as long as the package is not distributed outside of the firm. However, because this is a complicated matter with differing viewpoints, you should consult with your legal department first.

    How to include

    • As soon as you’ve confirmed that the licenses are compatible, you may include the code in your package for distribution. When doing so, you must ensure that all current license and copyright declarations are preserved, and that future readers can grasp the licensing position as easily as possible: If you’re including a fragment of another project, it’s typically better to put it in its own file and make sure that file contains copyright declarations and a license description at the beginning
    • if you’re including a fragment of another project, it’s generally best to put it in its own file
    • In the event that you’re included numerous files, group them together in a directory, and include a LICENSE file in that directory as well
    • You’ll also need to add some standard metadata in [email protected], which you can find here.
    • Using role = ″cph″ to declare that the author is a copyright holder, together with a note that describes what they’ve written, is recommended.
    • LICENSE.note is required if you’re submitting to CRAN and the bundled code has a different (but compatible) license from the rest of the package.
    • This file describes the overall license of the package and the specific licenses of each individual component.

    You must include a LICENSE.note file with your submission in order for it to be accepted by CRAN.To give an example, the diffviewer package contains six javascript libraries, all of which are distributed under a liberal license.The DESCRIPTION contains a list of all of the copyright holders, and the LICENSE.note contains information on their licenses.

    1. (Other packages employ different strategies, but I believe this is the most straightforward way that will be accepted by CRAN.)

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